[Congressional Bills 116th Congress]
[From the U.S. Government Publishing Office]
[H.R. 4355 Introduced in House (IH)]
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116th CONGRESS
1st Session
H. R. 4355
To direct the Director of the National Science Foundation to support
research on the outputs that may be generated by generative adversarial
networks, otherwise known as deepfakes, and other comparable techniques
that may be developed in the future, and for other purposes.
_______________________________________________________________________
IN THE HOUSE OF REPRESENTATIVES
September 17, 2019
Mr. Gonzalez of Ohio (for himself, Ms. Stevens, Mr. Baird, and Ms. Hill
of California) introduced the following bill; which was referred to the
Committee on Science, Space, and Technology
_______________________________________________________________________
A BILL
To direct the Director of the National Science Foundation to support
research on the outputs that may be generated by generative adversarial
networks, otherwise known as deepfakes, and other comparable techniques
that may be developed in the future, and for other purposes.
Be it enacted by the Senate and House of Representatives of the
United States of America in Congress assembled,
SECTION 1. SHORT TITLE.
This Act may be cited as the ``Identifying Outputs of Generative
Adversarial Networks Act'' or the ``IOGAN Act''.
SEC. 2. FINDINGS.
Congress finds the following:
(1) Research gaps currently exist on the underlying
technology needed to develop tools to identify authentic
videos, voice reproduction, or photos from those generated by
generative adversarial networks.
(2) The National Science Foundation's focus to support
research in artificial intelligence through computer and
information science and engineering, cognitive science and
psychology, economics and game theory, control theory,
linguistics, mathematics, and philosophy, is building a better
understanding of how new technologies are shaping the society
and economy of the United States.
(3) The National Science Foundation has identified the ``10
Big Ideas for NSF Future Investment'' including ``Harnessing
the Data Revolution'' and the ``Future of Work at the Human-
Technology Frontier'', in with artificial intelligence is a
critical component.
(4) The outputs generated by generative adversarial
networks should be included under the umbrella of research
described in paragraph (3) given the grave national security
and societal impact potential of such networks.
(5) Generative adversarial networks are not likely to be
utilized as the sole technique of artificial intelligence or
machine learning capable of creating credible deepfakes and
other comparable techniques may be developed in the future to
produce similar outputs.
SEC. 3. NSF SUPPORT OF RESEARCH FOR OUTPUTS OF GENERATIVE ADVERSARIAL
NETWORKS.
The Director of the National Science Foundation, in consultation
with other relevant Federal agencies, shall support merit-reviewed and
competitively awarded research on the science and ethics of material
produced by generative adversarial networks, which may include--
(1) supplementing fundamental research on digital media
forensic tools or comparable technologies for detection of the
outputs of generative adversarial networks completed by the
Defense Advanced Research Projects Agency and the Intelligence
Advanced Research Projects Activity;
(2) fundamental research on developing constraint aware
generative adversarial networks; and
(3) social and behavioral research on the ethics of the
technology, and human engagement with the networks.
SEC. 4. NIST SUPPORT FOR RESEARCH AND STANDARDS ON GENERATIVE
ADVERSARIAL NETWORKS.
(a) In General.--The Director of the National Institute of
Standards and Technology shall support research for the development of
measurements and standards necessary to accelerate the development of
the technological tools to examine the function and outputs of
generative adversarial networks.
(b) Outreach.--The Director of the National Institute of Standards
and Technology shall conduct outreach--
(1) to receive input from private, public, and academic
stakeholders on fundamental measurements and standards research
necessary to examine the function and outputs of generative of
generative adversarial networks or to develop constraint aware
generative adversarial networks; and
(2) to consider the feasibility of an ongoing public and
private sector engagement to develop voluntary standards for
the outputs of generative adversarial networks or comparable
technologies.
SEC. 5. REPORT ON FEASIBILITY OF PUBLIC-PRIVATE PARTNERSHIP TO DETECT
OUTPUTS OF GENERATIVE ADVERSARIAL NETWORKS AND COMPARABLE
TECHNOLOGIES.
Not later than one year after the date of the enactment of this
Act, the Director of the National Science Foundation and the Director
of the National Institute of Standards and Technology shall jointly
submit to the Committee on Science, Space, and Technology of the House
of Representatives and the Committee on Commerce, Science, and
Transportation a report containing--
(1) the Directors' findings with respect to the feasibility
for research opportunities with the private sector, including
digital media companies to detect the outputs of generative
adversarial networks or comparable technologies; and
(2) any policy recommendations of the Directors that could
facilitate and improve communication and coordination between
the private sector, the National Science Foundation, and
relevant Federal agencies through the implementation of
innovative approaches to detect media products produced by
generative adversarial networks or comparable technologies.
SEC. 6. DEFINITIONS.
In this Act:
(1) Generative adversarial network.--The term ``generative
adversarial network'' means, with respect to artificial
intelligence, the machine learning process of attempting to
cause a generator artificial neural network (referred to in
this paragraph as the ``generator'' and a discriminator
artificial neural network (referred to in this paragraph as a
``discriminator'')) to compete against each other to become
more accurate in their function and outputs, through which the
generator and discriminator create a feedback loop, causing the
generator to produce increasingly higher-quality artificial
outputs and the discriminator to increasingly improve in
detecting such artificial outputs.
(2) Comparable technology.--The term ``comparable
technology'' means technology that utilizes similar techniques
to achieve the same outputs as a generative adversarial
network.
(3) Constraint aware.--The term ``constraint aware'' means,
with respect to artificial intelligence, the generation of
realistic relational data by a machine with constraint on the
modules generated by an adversarial network.
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